Comparison of Global Algorithms in Word Sense Disambiguation
This work addresses the computational efficiency challenge in WSD for NLP applications, though it is incremental as it adapts existing algorithms to this domain.
The paper compared four probabilistic algorithms for Word Sense Disambiguation, finding that Cuckoo Search Algorithm achieved similar high F1 scores (up to 0.98) as others but with faster convergence, requiring fewer scorer calls to reach 0.95 F1.
This article compares four probabilistic algorithms (global algorithms) for Word Sense Disambiguation (WSD) in terms of the number of scorer calls (local algo- rithm) and the F1 score as determined by a gold-standard scorer. Two algorithms come from the state of the art, a Simulated Annealing Algorithm (SAA) and a Genetic Algorithm (GA) as well as two algorithms that we first adapt from WSD that are state of the art probabilistic search algorithms, namely a Cuckoo search algorithm (CSA) and a Bat Search algorithm (BS). As WSD requires to evaluate exponentially many word sense combinations (with branching factors of up to 6 or more), probabilistic algorithms allow to find approximate solution in a tractable time by sampling the search space. We find that CSA, GA and SA all eventually converge to similar results (0.98 F1 score), but CSA gets there faster (in fewer scorer calls) and reaches up to 0.95 F1 before SA in fewer scorer calls. In BA a strict convergence criterion prevents it from reaching above 0.89 F1.